Cited by 100Impact of Technology on AdolescentsEthics and Social Impacts of AIInnovative Human-Technology Interaction

Can ChatGPT Be Addictive? A Call to Examine the Shift from Support to Dependence in AI Conversational Large Language Models

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Yankouskaya, Ala, Liebherr, Magnus, Ali, Raian·2025·Human-Centric Intelligent Systems

Abstract

The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models. This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.

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  • A substantial body of scholarship has identified the asymmetry of AI companionship as a mechanism through which AI companions may foster unrealistic relational expectations, displace effortful human intimacy, and normalize one-sided emotional dynamics.

    “A substantial body of scholarship has identified this asymmetry as a mechanism through which AI companions may foster unrealistic relational expectations, displace effortful human intimacy, and normalize one-sided emotional dynamics (Turkle, 2011; Malfacini, 2025; Yankouskaya et al., 2025).”

    From Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship (2026) · cites this paper as Yankouskaya et al., 2025

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  • Existing research on chatbots and relational AI has reflected a risk-oriented framing.

    “Existing research on chatbots and relational AI has reflected a risk-oriented framing (Chianella et al., 2021; Yankouskaya et al., 2025).”

    From Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship (2026) · cites this paper as Yankouskaya et al., 2025

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  1. Ebert C, Louridas P. Generative AI for software practitioners. IEEE Softw. 2023;40(4):30-8. https:// doi. org/ 10. 1109/ MS. 2023. 32658 77. · DOI · Paper page
  2. Thoppilan R, De Freitas D, Hall J, Shazeer NM, Kulshreshtha et al. LaMDA: language models for dialog applications. arXiv. 2022. https:// arxiv. org/ abs/ 2201. 08239. Accessed 20 Jan 2025.
  3. Xie Y, Zhao S, Zhou P, Liang C. Understanding contin- ued use intention of AI assistants. J Comput Inform Syst. 2023;63(6):1424-37. https:// doi. org/ 10. 1080/ 08874 417. 2023. 21671 34. · DOI
  4. Björling EA, Ling H, Bhatia S, Matarrese J. Sharing stressors with a social robot prototype: what embodiment do adolescents prefer? Int J Child Comput Interact. 2021;28:1-11. https:// doi. org/ 10. 1016/j. ijcci. 2021. 100252. · DOI
  5. Liu AR, Pataranutaporn P, Turkle S, Maes P. Chatbot companion- ship: a mixed-methods study of companion chatbot usage pat- terns and their relationship to loneliness in active users. arXiv preprint. 2024; arXiv: 2410. 21596.
  6. Ng YL. Exploring the association between use of conversational artificial intelligence and social capital: survey evidence from Hong Kong. New Media Soc. 2024;26(3):1429-44. https:// doi. org/ 10. 1177/ 14614 44822 10740 47. · DOI
  7. Campanella S. Addictive behaviors: decades of research, but still so many questions! Front Psychol. 2024;15:1485118. https:// doi. org/ 10. 3389/ fpsyg. 2024. 14851 18. · DOI
  8. Sussman S. Workaholism: a review. J Addict Res Ther. 2012;6(1):4120. https:// doi. org/ 10. 4172/ 2155-6105. S6-001. · DOI
  9. Alavi SS, Ferdosi M, Jannatifard F, Eslami M, Alaghemandan H, Setare M. Behavioral addiction versus substance addiction: cor- respondence of psychiatric and psychological views. Int J Prev Med. 2012;3(4):290-4.
  10. Ding K, Shen Y, Liu Q, Li H. The effects of digital addiction on brain function and structure of children and adolescents: a scop- ing review. Healthcare (Basel). 2023;12(1):15. https:// doi. org/ 10. 3390/ healt hcare 12010 015. · DOI
  11. Heilig M, MacKillop J, Martinez D, et al. Addiction as a brain disease revised: why it still matters, and the need for consilience. Neuropsychopharmacology. 2021;46(11):1715-23. https:// doi. org/ 10. 1038/ s41386-020-00950-y. · DOI
  12. Griffiths MD, Kuss DJ, Billieux J, Pontes H. The evolution of Internet addiction: a global perspective. Addict Behav. 2016;53:193-5. https:// doi. org/ 10. 1016/j. addbeh. 2015. 11. 001. · DOI
  13. Salah M, Abdelfattah F, Al HH. The good, the bad, and the GPT: reviewing the impact of generative artificial intelligence on psy- chology. Curr Opin Psychol. 2024;59: 101872. https:// doi. org/ 10. 1016/j. copsyc. 2024. 101872. · DOI
  14. Sundar SS, Lee E-J. Rethinking communication in the era of artificial intelligence. Hum Commun Res. 2022;48(3):379-85. https:// doi. org/ 10. 1093/ hcr/ hqac0 14. · DOI
  15. Qin P, Liu Y, Shi J, Wang Y, Duncan N, Gong Q, Weng X, Northoff G. Dissociation between anterior and posterior corti- cal regions during self-specificity and familiarity: a combined fMRI-meta-analytic study. Hum Brain Mapp. 2012;33(1):154- 64. https:// doi. org/ 10. 1002/ hbm. 21201. · DOI
  16. Northoff G. Is the self a higher-order or fundamental function of the brain? The "basis model of self-specificity" and its encoding by the brain's spontaneous activity. Cogn Neurosci. 2016;7(1- 4):203-22. https:// doi. org/ 10. 1080/ 17588 928. 2015. 11118 68. · DOI
  17. Hillman JG, Fowlie DI, MacDonald TK. Social Verification Theory: a new way to conceptualize validation, dissonance, and belonging. Pers Soc Psychol Rev. 2023;27(3):309-31. https:// doi. org/ 10. 1177/ 10888 68322 11383 84. · DOI
  18. Schoeller F, Christov-Moore L, Lynch C, Diot T, Reggente N. Predicting individual differences in peak emotional response. PNAS Nexus. 2024;3(3):pgae066. https:// doi. org/ 10. 1093/ pnasn exus/ pgae0 66. · DOI
  19. Stets JE, Burke PJ. Self-esteem and identities. Sociol Perspect. 2014;57(4):409-33. https:// doi. org/ 10. 1177/ 07311 21414 536141. · DOI
  20. Nakamura J, Csikszentmihalyi M. The concept of flow. In: Flow and the foundations of positive psychology. Dordrecht: Springer; 2014. p. 239-63. https:// doi. org/ 10. 1007/ 978-94-017-9088-8_ 16. · DOI · Paper page
  21. Gold J, Ciorciari J. A review on the role of the neurosci- ence of flow states in the modern world. Behav Sci (Basel). 2020;10(9):137. https:// doi. org/ 10. 3390/ bs100 90137. · DOI
  22. Finneran CM, Zhang P. A person-artefact-task (PAT) model of flow antecedents in computer-mediated environments. Int J Hum Comput Stud. 2003;59(4):475-96. https:// doi. org/ 10. 1016/ S1071-5819(03) 00112-5. · DOI
  23. Youn S, Jin SV. In AI we trust?" The effects of parasocial interac- tion and technopian versus luddite ideological views on chatbot- based customer relationship management in the emerging "feel- ing economy. Comput Hum Behav. 2021;119: 106721. https:// doi. org/ 10. 1016/j. chb. 2021. 106721. · DOI
  24. Lotun S, Lamarche VM, Matran-Fernandez A, et al. People per- ceive parasocial relationships to be effective at fulfilling emo- tional needs. Sci Rep. 2024;14:8185. https:// doi. org/ 10. 1038/ s41598-024-58069-9. · DOI
  25. Giles DC, Maltby J. The role of media figures in adolescent development: relations between autonomy, attachment, and inter- est in celebrities. Pers Individ Differ. 2004;36(4):813-22. https:// doi. org/ 10. 1016/ S0191-8869(03) 00154-5. · DOI
  26. Skumanich SA, Kintsfather DP. Individual media dependency relations within television shopping programming: a causal model reviewed and revised. Commun Res. 1998;25(2):200-19. https:// doi. org/ 10. 1177/ 00936 50980 25002 004. · DOI
  27. Giles DC. Parasocial interaction: a review of the literature and a model for future research. Media Psychol. 2002;4(3):279-305. https:// doi. org/ 10. 1207/ S1532 785XM EP0403_ 04. · DOI · Paper page
  28. Dibble JL, Hartmann T, Rosaen SF. Parasocial interaction and parasocial relationship: conceptual clarification and a critical assessment of measures. Hum Commun Res. 2016;42(1):21-44. https:// doi. org/ 10. 1111/ hcre. 12063. · DOI
  29. Ferrari F, Paladino MP, Jetten J. Blurring human-machine dis- tinctions: anthropomorphic appearance in social robots as a threat to human distinctiveness. Int J Soc Robot. 2016;8(3):287- 302. https:// doi. org/ 10. 1007/ s12369-016-0338-y. · DOI
  30. Krach S, Hegel F, Wrede B, Sagerer G, Binkofski F, Kircher T. Can machines think? Interaction and perspective taking with robots investigated via fMRI. PLoS ONE. 2008;3(7): e2597. https:// doi. org/ 10. 1371/ journ al. pone. 00025 97. · DOI
  31. Rubin AM, Perse EM, Powell RA. Loneliness, parasocial inter- action, and local television news viewing. Hum Commun Res. 1985;12(2):155-80. https:// doi. org/ 10. 1111/j. 1468-2958. 1985. tb000 71.x. · DOI
  32. Govindaraju V. Media psychology approaches in digitalization and human communication. In: Promoting diversity, equity, and inclusion in language learning environments. IGI Global Infor- mation Science Reference; 2023. pp. 193-202. https:// doi. org/ 10. 4018/ 978-1-6684-3632-5. ch012. · DOI
  33. Nicolas S, Agnieszka W. The personality of anthropomorphism: how the need for cognition and the need for closure define atti- tudes and anthropomorphic attributions toward robots. Comput Hum Behav. 2021;122: 106841. https:// doi. org/ 10. 1016/j. chb. 2021. 106841. · DOI
  34. Nazar M, Alam MM, Yafi E, Su'ud MM. A systematic review of human-computer interaction and explainable artificial intel- ligence in healthcare with artificial intelligence techniques. IEEE Access. 2021;9:153316-48. https:// doi. org/ 10. 1109/ ACCESS. 2021. 31278 81. · DOI · Paper page
  35. Cook KS, Cheshire C, Rice ERW, Nakagawa S. Social exchange theory. In: DeLamater J, Ward A, editors. Handbook of social psychology. 2nd ed. Berlin: Springer; 2013. p. 61-88. https:// doi. org/ 10. 1007/ 978-94-007-6772-0_3. · DOI
  36. Feeney BC, Collins NL. A new look at social support: a theo- retical perspective on thriving through relationships. Pers Soc Psychol Rev. 2015;19(2):113-47. https:// doi. org/ 10. 1177/ 10888 68314 544222. · DOI · Paper page
  37. Levy N, Harmon-Jones C, Harmon-Jones E. Dissonance and dis- comfort: does a simple cognitive inconsistency evoke a negative affective state? Motiv Sci. 2018;4(2):95-108. https:// doi. org/ 10. 1037/ mot00 00079. · DOI
  38. Hobfoll SE. Conservation of resources theory: Its implication for stress, health, and resilience. In: Folkman S, editor. The Oxford handbook of stress, health, and coping. Oxford: Oxford Univer- sity Press; 2011. p. 127-47.
  39. Vaillant GE. Ego mechanisms of defense and personality psy- chopathology. J Abnorm Psychol. 1994;103(1):44-50. https:// doi. org/ 10. 1037/ 0021-843x. 103.1. 44. · DOI
  40. Coughlin SS. Recall bias in epidemiologic studies. J Clin Epide- miol. 1990;43(1):87-91. https:// doi. org/ 10. 1016/ 0895-4356(90) 90060-3. · DOI · Paper page
  41. King R. Cognitive therapy of depression. Aaron Beck, John Rush, Brian Shaw, Gary Emery. New York: Guilford, 1979. Aust N Z J Psychiatry. 2002;36(2):272-275. https:// doi. org/ 10. 1046/j. 1440-1614. 2002. t01-4-01015.x · DOI
  42. Gu Z, Chupradit S, Ku KY, Nassani AA, Haffar M. Impact of employees' workplace environment on employees' performance: a multi-mediation model. Front Public Health. 2022;10: 890400. https:// doi. org/ 10. 3389/ fpubh. 2022. 890400. · DOI
  43. Liu Y, Yang Y, Bai X, Chen Y, Mo L. Do immediate exter- nal rewards really enhance intrinsic motivation? Front Psychol. 2022;13: 853879. https:// doi. org/ 10. 3389/ fpsyg. 2022. 853879. · DOI
  44. Woolley K, Fishbach A. Immediate rewards predict adherence to long-term goals. Pers Soc Psychol Bull. 2017;43(2):151-62. https:// doi. org/ 10. 1177/ 01461 67216 676480. · DOI · Paper page
  45. Gao Z, Wang H, Lu C, et al. The neural basis of delayed gratifi- cation. Sci Adv. 2021;7(49):eabg6611. https:// doi. org/ 10. 1126/ sciadv. abg66 11. · DOI
  46. Bromberg-Martin ES, Matsumoto M, Hikosaka O. Dopamine in motivational control: rewarding, aversive, and alerting. Neuron. 2010;68(5):815-34. https:// doi. org/ 10. 1016/j. neuron. 2010. 11. 022. · DOI · Paper page
  47. Yang X, Qiu D, Lau MCM, Lau JTF. The mediation role of work- life balance stress and chronic fatigue in the relationship between workaholism and depression among Chinese male workers in Hong Kong. J Behav Addict. 2020;9(2):483-90. https:// doi. org/ 10. 1556/ 2006. 2020. 00026. · DOI
  48. Andreassen CS. Workaholism: an overview and current status of the research. J Behav Addict. 2014;3(1):1-11. https:// doi. org/ 10. 1556/ JBA.2. 2013. 017. · DOI
  49. Molino M, Cortese CG, Ghislieri C. Daily effect of recovery on exhaustion: a cross-level interaction effect of workaholism. Int J Environ Res Public Health. 2018;15(9):1920. https:// doi. org/ 10. 3390/ ijerp h1509 1920. · DOI
  50. Morkevičiūtė M, Endriulaitienė A. The role of an individual and a situation in explaining work addiction: disclosing complex rela- tions. Int J Environ Res Public Health. 2023;20(5):4560. https:// doi. org/ 10. 3390/ ijerp h2005 4560. · DOI
  51. Pluut H, Wonders J. Not able to lead a healthy life when you need it the most: dual role of lifestyle behaviors in the association of blurred work-life boundaries with well-being. Front Psychol. 2020;11: 607294. https:// doi. org/ 10. 3389/ fpsyg. 2020. 607294. · DOI
  52. Starcevic V, Berle D. Cyberchondria: towards a better under- standing of excessive health-related Internet use. Expert Rev Neurother. 2013;13(2):205-13. https:// doi. org/ 10. 1586/ ern. 12. 162. · DOI · Paper page
  53. Fechner HB, Schooler LJ, Pachur T. Cognitive costs of decision- making strategies: a resource demand decomposition analysis with a cognitive architecture. Cognition. 2018;170:102-22. https:// doi. org/ 10. 1016/j. cogni tion. 2017. 09. 003. · DOI
  54. Skagerlund K, Forsblad M, Tinghög G, Västfjäll D. Decision- making competence and cognitive abilities: which abilities mat- ter? J Behav Decis Mak. 2022;35(1): e2242. https:// doi. org/ 10. 1002/ bdm. 2242. · DOI · Paper page
  55. Kool W, McGuire JT, Rosen ZB, Botvinick MM. Decision mak- ing and the avoidance of cognitive demand. J Exp Psychol Gen. 2010;139(4):665-82. https:// doi. org/ 10. 1037/ a0020 198. · DOI · Paper page
  56. Vicente L, Matute H. Humans inherit artificial intelligence biases. Sci Rep. 2023;13:15737. https:// doi. org/ 10. 1038/ s41598-023-42384-8. · DOI · Paper page
  57. Wiczorek R, Meyer J. Effects of trust, self-confidence, and feedback on the use of decision automation. Front Psychol. 2019;10:519. https:// doi. org/ 10. 3389/ fpsyg. 2019. 00519. · DOI · Paper page
  58. Jussupow E, Spohrer K, Heinzl A, Gawlitza J. Augmenting medi- cal decision-making? How delegation to AI influences decision- making under uncertainty. Inf Syst Res. 2021;32:713-35. https:// doi. org/ 10. 1287/ isre. 2020. 0980. · DOI · Paper page
  59. Sele D, Chugunova M. Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making. PLoS ONE. 2024;19: e0298037. https:// doi. org/ 10. 1371/ journ al. pone. 02980 37. · DOI
  60. Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, et al. Opinion Paper: "So what if ChatGPT wrote it?" Multidis- ciplinary perspectives on opportunities, challenges and implica- tions of generative conversational AI for research, practice and policy. Int J Inf Manage. 2023;71: 102642. https:// doi. org/ 10. 1016/j. ijinf omgt. 2023. 102642. · DOI · Paper page
  61. Liaw W, Chavez S, Pham C, Tehami S, Govender R. The haz- ards of using ChatGPT: a call to action for medical education researchers. PRiMER. 2023;7:27. https:// doi. org/ 10. 22454/ PRiMER. 2023. 295710. · DOI
  62. Hasanein AM, Sobaih AEE. Drivers and consequences of Chat- GPT use in higher education: key stakeholder perspectives. Eur J Investig Health Psychol Educ. 2023;13:2599-614. https:// doi. org/ 10. 3390/ ejihp e1311 0181. · DOI
  63. Haman M, Školník M. Behind the ChatGPT hype: are its suggestions contributing to addiction? Ann Biomed Eng. 2023;51:1128-9. https:// doi. org/ 10. 1007/ s10439-023-03201-5. · DOI
  64. Yu SC, Chen HR, Yang YW. Development and valida- tion of the problematic ChatGPT Use Scale: a preliminary report. Curr Psychol. 2024;43:1-13. https:// doi. org/ 10. 1007/ s12144-024-06259-z. · DOI

Cited by

Sorted by citations

Authors
Yankouskaya, A., Liebherr, M., & Ali, R.
Affiliations
  • Bournemouth University (GB)
  • University of Duisburg-Essen (DE)
  • Hamad bin Khalifa University (QA)
Corresponding author
Yankouskaya, Ala
Venue
Human-Centric Intelligent Systems
Published
2025-02-17
Volume
5
Issue
1
Pages
77-89
ISSN
2667-1336 (ISSN-L)
OpenAlex
W4407637219
Cited by
100
References
31
Citations by year
2026: 72 · 2025: 28
FWCI
172.99
Type
journal article
Language
en
Open access
Locations
  • Human-Centric Intelligent Systems (publishedVersion, cc-by)
  • Bournemouth University Research Online (Bournemouth University) (acceptedVersion)
  • Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Qatar National Research Fund (NPRP 14C-0916–210015)
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
ChatGPT, AI dependency, large language models, overreliance on AI, critical thinking, emotional validation, tolerance (pharmacology and immunology), human-AI interaction

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